Re: 2 Variables, 7 cases, 10 observations -- Simple?

Posted by Rich Ulrich on Mar 06, 2018; 6:37am
URL: http://spssx-discussion.165.s1.nabble.com/2-Variables-7-cases-10-observations-Simple-tp5735614p5735634.html

If I understand how you ran the statistics that you ran, in no case did you look at the within-subject correlation. So, in no case did you have a test based on your sample size of 7, but, rather, on the 50-some periods of observation. That does not give a valid test. If you want a between-subject correlation, look at the r for the sample of 7 scores, one per person. "People who are higher on the one are also higher on the other" is the between-subject test.

The discriminant function that I suggested earlier will give you the "within-subjects correlation", which removes the mean levels for each subject. It will give you a valid test.

When I first looked at the scores, I wondered if the point-differences at the low end should be more important than the point-differences at the high end. Since these scores are described as counts, you will likely have a more robust analysis - and one where equal intervals are better respected - if you take the square-root of each count as your score to use in an analysis.

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Rich Ulrich

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From: SPSSX(r) Discussion <[hidden email]> on behalf of PsyDStats <[hidden email]>
Sent: Monday, March 5, 2018 10:47:46 AM
To: [hidden email]
Subject: Re: 2 Variables, 7 cases, 10 observations -- Simple?

 

First of all, THANK you very much for everyone's responses.

Secondly, yes, an N of 7 is sparse. The 0 means no observations. I'm
hesitant to remove St1 because of the already small sample size, even though
it lacks . I oriented the data vertically as in my first scenario and ran
nonparametric stats comparing AN with HA. The scatter plot showed a
monotonic relationship , so I ran Spearmans rho. I thought the higher
specificity of Kendall's Tau would be useful, because of its accuracy over
Spearman with smaller sample sizes. I also ran Somer's d, since I have
dependent and independent variables.  All showed positive correlations at
.001. However, I'm worried that I'm missing something more essential with my
data or that I've missed assumptions that made these metrics inappropriate
to begin with. From your responses, I'm even more nervous.

Thank you again for the interest in my situation and your helpful insights.
I wish my committee and those I approached for participation were as engaged
as you are.



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